Nearest Kronecker Product Decomposition Based Generalized Maximum Correntropy and Generalized Hyperbolic Secant Robust Adaptive Filters

Robust adaptive signal processing algorithms based on a generalized maximum correntropy criterion (GMCC) suffers from high steady state misalignment. In an endeavour to achieve lower steady state misalignment, in this letter we propose a generalized hyperbolic secant function (GHSF) as a robust norm and derive the generalized hyperbolic secant adaptive filter (GHSAF). The new algorithm is seen to offer robust system identification performance over the conventional GMCC algorithm. To further improve the convergence performance under non-Gaussian noise environments, we propose the nearest Kronecker product decomposition based GMCC and GHSAF algorithms. Extensive simulation study show the improved convergence performance provided by the proposed algorithms for system identification.

[1]  Yingsong Li,et al.  Maximum Correntropy Criterion With Variable Center , 2019, IEEE Signal Processing Letters.

[2]  Badong Chen,et al.  A Separable Maximum Correntropy Adaptive Algorithm , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[3]  Nithin V. George,et al.  Nearest Kronecker Product Decomposition Based Normalized Least Mean Square Algorithm , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[4]  G. Wittum,et al.  Adaptive filtering , 1997 .

[5]  Markus Rupp,et al.  A tensor LMS algorithm , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Jacob Benesty,et al.  Recursive Least-Squares Algorithms for the Identification of Low-Rank Systems , 2019, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[7]  W. Harkness,et al.  Generalized hyperbolic secant distributions , 1968 .

[8]  Jacob Benesty,et al.  A Recursive Least-squares Algorithm Based on the Nearest Kronecker Product Decomposition , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Wei Xing Zheng,et al.  A Family of Robust M-Shaped Error Weighted Least Mean Square Algorithms: Performance Analysis and Echo Cancellation Application , 2017, IEEE Access.

[10]  Yi Yu,et al.  Diffusion Normalized Least Mean M-estimate Algorithms: Design and Performance Analysis , 2020, IEEE Transactions on Signal Processing.

[11]  D. Harville.  Matrix Algebra From a Statistician's Perspective , 1998 .

[12]  Weifeng Liu,et al.  Correntropy: Properties and Applications in Non-Gaussian Signal Processing , 2007, IEEE Transactions on Signal Processing.

[13]  Konstantinos Slavakis,et al.  Robust Hierarchical-Optimization RLS Against Sparse Outliers , 2019, IEEE Signal Processing Letters.

[14]  Kunde Yang,et al.  Sparse Estimator With $\ell_0$ -Norm Constraint Kernel Maximum-Correntropy-Criterion , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[15]  Liming Shi,et al.  Convex Combination of Adaptive Filters under the Maximum Correntropy Criterion in Impulsive Interference , 2014, IEEE Signal Processing Letters.

[16]  Badong Chen,et al.  A New Robust Kalman Filter With Adaptive Estimate of Time-Varying Measurement Bias , 2020, IEEE Signal Processing Letters.

[17]  Xifeng Li,et al.  A Sparse Robust Adaptive Filtering Algorithm Based on the $q$-Rényi Kernel Function , 2020, IEEE Signal Processing Letters.

[18]  Le Gao,et al.  A $ q$-Gaussian Maximum Correntropy Adaptive Filtering Algorithm for Robust Spare Recovery in Impulsive Noise , 2018, IEEE Signal Processing Letters.

[19]  Nanning Zheng,et al.  Generalized Correntropy for Robust Adaptive Filtering , 2015, IEEE Transactions on Signal Processing.

[20]  Haiquan Zhao,et al.  Robust Distributed Diffusion Recursive Least Squares Algorithms With Side Information for Adaptive Networks , 2018, IEEE Transactions on Signal Processing.

[21]  Jacob Benesty,et al.  An efficient Kalman filter for the identification of low-rank systems , 2020, Signal Process..

[22]  Sheng Zhang,et al.  Maximum Versoria Criterion-Based Robust Adaptive Filtering Algorithm , 2017, IEEE Transactions on Circuits and Systems II: Express Briefs.

[23]  Jacob Benesty,et al.  Linear System Identification Based on a Kronecker Product Decomposition , 2018, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[24]  Nanning Zheng,et al.  Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering , 2016, IEEE Transactions on Signal Processing.